Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Detection of Signal in the Spiked Rectangular Models
Authors: Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the improvement of PCA in Section 3.1, we perform the following numerical experiment: We choose a vector z R784 from the standard Fashion-MNIST dataset. ... In Fig. 1, we compare the reconstruction by the improved PCA with standard PCA over Y. With the optimal entrywise transformation, the proposed PCA outperforms the standard PCA. For more simulation results about the improved PCA, see Section A of Supplementary Material. In Figure 3, we plot empirical average (after 10,000 Monte Carlo simulations) of the error of the proposed test in Algorithm 1 and the theoretical (limiting) error in (15), varying the SNR ω from 0 to 0.5, with M = 256 and N = 512. |
| Researcher Affiliation | Academia | 1Department of Mathematical Sciences, KAIST, Daejeon, Korea 2School of Electrical Engineering, KAIST, Daejeon, Korea 3School of Mathematics, KIAS, Seoul, Korea. |
| Pseudocode | Yes | Algorithm 1 Hypothesis test |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We choose a vector z R784 from the standard Fashion-MNIST dataset. |
| Dataset Splits | No | The paper mentions using Fashion-MNIST and other synthetic data for experiments but does not specify train/validation/test splits, percentages, or methodology for splitting the data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as programming languages, libraries, or solvers with version numbers, that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper describes the construction of the data matrix and the noise characteristics for numerical experiments but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, epochs), optimizer settings, or other system-level training configurations. |